ARJUN PRAKASH · RESEARCH SYSTEM ONLINE 00:00:00 UTC

ONLINE ADAPTATION & CONTINUAL LEARNING

I’m Arjun. I research reinforcement learning, game theory, and robotics. My aim is to build collaborative and safe autonomous robots that can continuously adapt to changing worlds.

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01THESIS

The world doesn’t hold still. Learning can’t either.

Learning systems should do more than find a solution once. They should preserve the capacity to adapt as objectives shift, environments change, and other learners respond.

My work studies the dynamics, representations, and interactions that make continued adaptation possible—and the failures that make it disappear.

02RESEARCH 01 · SPECTRAL COLLAPSE
PAPER 01 SEP · 2025 ARXIV:2509.22335
CONTINUAL LEARNING · CURVATURE

Spectral Collapse Drives Loss of Plasticity in Deep Continual Learning

Arjun Prakash · Naicheng He · Kaicheng Guo · Saket Tiwari · Ruo Yu Tao · Tyrone Serapio · Amy Greenwald · George Konidaris

IDEA IN MOTION

A neural network can slowly become too rigid to learn anything new. Here, that loss of flexibility appears as a cloud collapsing from many directions into none.

THE PAPER IN BRIEF

Neural networks do not always stay ready to learn. When trained on a stream of new tasks, their internal features can become less flexible until new learning barely works. This paper connects that loss of flexibility to a collapse in the network’s useful directions and explores ways to preserve them for whatever comes next.

3D SPACE · DIMENSION 3 4,096 PARTICLES · WEBGL2
ALL DIRECTIONS OPEN

A deliberately stylized metaphor for dimensionality—not a numerical reconstruction. The same 4,096 particles keep moving, but each collapse removes a direction permanently until the sequence is reset.

03RESEARCH 02 · BILEVEL POLICY OPTIMIZATION
PAPER 02 MAY · 2025 ARXIV:2505.11714
REINFORCEMENT LEARNING · BILEVEL OPTIMIZATION

Bi-Level Policy Optimization with Nyström Hypergradients

Arjun Prakash · Naicheng He · Denizalp Goktas · Jacob Makar-Limanov · Amy Greenwald

IDEA IN MOTION

When two parts of a learning system adapt to each other, the order of their updates can determine whether they wander, cycle, or settle.

LIVE SIMULATION
THE PAPER IN BRIEF

An actor decides what to do and a critic judges those decisions. Because both are learning, each update changes the problem faced by the other. This paper treats that relationship as a nested process, allowing the actor to anticipate the critic’s response and move toward more stable solutions.

This is the paper’s toy experiment running live. Each panel starts from the same points, then follows a different learning rule. In the first two, the learners keep circling one another. In the last two, they settle into the same stable outcome. Click anywhere to push the particles apart and watch every rule respond to the same new starting conditions.

04RESEARCH 03 · REACH–AVOID STACKELBERG GAME
PAPER 03 NEURIPS · 2023 ARXIV:2401.12437
MULTI-AGENT RL · GAME THEORY

Convex-Concave Zero-Sum Markov Stackelberg Games

Denizalp Goktas · Arjun Prakash · Amy Greenwald

IDEA IN MOTION

Two cars learn through competition: one searches for a route to the goal, while the other learns to intercept it.

THE PAPER IN BRIEF

Many decisions are made against another learner. This paper studies games in which one player commits to a strategy and the other responds, then develops practical ways for both to learn from experience. The reach–avoid experiment turns that idea into a pursuit: reach the target before the opponent can intervene.

INITIALIZING SELF-PLAY SPARSE REWARD · ALT 4:4 · TWO POLICIES LEARNING LIVE 0 STEPS/S
UPDATE SCHEDULE
4 REACH BATCHES · 4 DEFEND BATCHES
UPDATE000
SELF-PLAY GAMES0000
REACH WINS · 50 AVG0%
CAPTURES · 50 AVG0%
OUTCOME TIMELINE · ALL SELF-PLAY GAMES · 12-GAME ROLLING RATE

Both cars learn from scratch while this tab is open. They receive feedback only when a run ends: the reach car succeeds at the goal and loses if it is captured or runs out of time, while the defender receives the opposite result. Alternating mode lets them take turns learning. Nested mode gives the reach car several attempts to adapt before the defender responds. Each mode keeps its own live history, and the world wraps cleanly at every edge.

05RESEARCH 04 · STRUCTURAL CLUSTERING
PAPER 04 AMF · 2022 ARXIV:2004.09963
QUANTITATIVE FINANCE · CHANGE POINTS

Structural Clustering of Volatility Regimes for Dynamic Trading Strategies

Arjun Prakash · Nick James · Max Menzies · Gilad Francis

IDEA IN MOTION

A noisy signal can change character without warning. The detector spots those shifts and colours the stream by how calm or turbulent it has become.

THE PAPER IN BRIEF

Markets do not behave the same way forever. This paper detects moments when their behavior changes, groups similar periods together, and uses those recurring patterns to adjust risk over time—without deciding in advance how many kinds of market there are.

WATCHING FOR CHANGE LIVE SIGNAL · CHANGE DETECTION SAMPLE 000
DETECTOR SIGNALQUIET
LAST CHANGE—
CONFIRMATION LAG—
REGIMES FOUND01

The line never stops. The detector continually compares recent behaviour with what came before and marks a new regime when that difference becomes convincing. Cyan shows calmer periods, lime shows active periods, and coral shows turbulence. The colours are a visual guide to the intensity of the signal; the paper goes further by grouping similar historical regimes and using them to manage risk.

LIVE TECHNICAL COLOPHON

The research is
running on your
device.

Nothing here is prerecorded. Each visualization is generated live in your browser from the ideas, dynamics, and experiments behind the papers.

RENDERERDETECTING
COLOR SPACESRGB
FRAME RATE— FPS
MOTIONACTIVE
POINTER X/Y0.00 / 0.00
SCROLL DEPTH000%
END OF THIS TASKREADY FOR THE NEXT

NEVER STOP LEARNING

Designed and computed at the edge.
One page. Live systems. Always adapting.

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